• Journal of Digital Convergence
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• v.20 no.4
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• pp.747-758
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• 2022

This study aimed to identify the factors that influence depression in stressed adults by age. Data were extracted from the 7th Korea National Health and Nutrition Examination Survey, covering 3,333 adults aged 20 to 59 years who were highly aware of stress. Linear regression analysis was performed using the IBM SPSS 25.0 program. The study found that in the case of people in their 20s, education level, health-related quality of life, obesity, weight change, smoking, and subjective body type were significant influencing factors, with an explanatory power of 60.3%. In the case of people in their 30s, gender, household income level, living with spouse, economic activity, health-related quality of life, food intake, obesity, alcohol consumption, smoking, and subjective health were significant influencing factors, with an explanatory power of 30.3%. For people in their 40s, household income level, living with spouse, economic activity, health-related quality of life, smoking, aerobic exercise, and subjective health were significant influencing factors, with an explanatory power of 34.4%. For people in their 50s, gender, education level, income, economic activity, health-related quality of life, protein intake, fat intake, high blood pressure, diabetes, weight control, aerobic exercise, subjective health, and subjective body type were significant influencing factors, with an explanatory power of 42.3%. Therefore, as it was found through this study that the factors affecting depression in stressed adults differ by age, it is necessary to establish an intervention strategy for each age when trying to lower depression in stressed adults.

• Journal of Digital Convergence
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• v.11 no.4
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• pp.351-359
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• 2013

This study was conducted to investigate the factors that affect the length of stay in hospital for schizophrenic patients. Of the data from the in-depth injury patient surveillance system, the final subject included 2,239 patients with schizophrenia in their final diagnosis. Using SPSS 18.0, a hierarchical regression analysis was performed by sequentially entering the explanatory variables by setting sociodemographic characteristics, discharge characteristics and hospital characteristics as explanatory variables and the length of stay in hospital as a dependent variable. The findings showed that the sociodemographic characteristics had the highest explanatory power and the explanatory power changed when the explanatory variable of the hospital characteristics was added, as opposed to the discharge characteristics. Male, type-1 medicaid, Chungcheong-do and the number of beds were found to be the factors that mostly affect the length of stay. Since this study used the secondary data, it has a limitation in terms of additional variables that could better explain the length of stay for schizophrenic patients. Nevertheless, it has an implication in that it investigated a large scale of data on a national level. For the effort of reducing the length of stay, it is suggested that an effort should be made at the national level, by focusing more on the hospital characteristics as well as the individual characteristics of patients.

• Journal of Korean Academy of Nursing
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• v.41 no.4
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• pp.460-470
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• 2011

Purpose: The aim of this study was to develop and test an explanatory model for sleep disorders in people with cancer. A hypothetical model was constructed on the basis of a review of previous studies, literature, and sleep models, and 10 latent variables were used to construct a hypothetical model. Methods: Data were collected from April 19 to June 25, 2010, using self-report questionnaires. The sample was 291 outpatients with cancer who visited the oncology cancer center at a university hospital. Collected data were analyzed using SPSS Win 15.0 program for descriptive statistics and correlation analysis and AMOS 7.0 program for covariance structural analysis. Results: It appeared that overall fit index was good as ${\chi}^2/df=1.162$, GFI=.969, AGFI=.944, SRMR=.052, NFI=.881, NNFI=.969, CFI=.980, RMSEA=.024, CN=337 in the modified model. The explanatory power of this model for sleep disorders in people with cancer was 62%. Further, sleep disorders were influenced directly by cancer symptom experience, dysfunctional beliefs and attitudes about sleep, and past sleep pattern. Conclusion: Findings suggest that nurses should assess past sleep pattern and consider the development of a comprehensive nursing intervention program to minimize the cancer symptom experience, dysfunctional beliefs and attitudes about sleep, and thus, reduce sleep disorders in people with cancer.

• Bulletin of the Korea Photovoltaic Society
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• v.4 no.2
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• pp.45-53
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• 2018

Natural gas demand for power generation continued to increase until 2013 due to the expansion of large-scale LNG power plants after the black-out of 2011. However, natural gas demand for power generation has decreased sharply due to the increase of nuclear power and coal power generation. But demand for power generation has increased again as energy policies have changed, such as reducing nuclear power and coal power plants, and abnormal high temperatures and cold waves have occurred. If the gas pipeline pressure can be properly maintained by predicting these fluctuations, it can contribute to enhancement of operation efficiency by minimizing the operation time of facilities required for production and supply. In this study, we have developed a regression model with daily power demand and base power generation capacity as explanatory variables considering characteristics by day of week. The model was constructed using data from January 2013 to December 2016, and it was confirmed that the error rate was 4.12% and the error rate in the 90th percentile was below 8.85%.

• Journal of the Korean Geographical Society
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• v.51 no.2
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• pp.219-233
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• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Research in Community and Public Health Nursing
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• v.28 no.3
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• pp.313-323
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• 2017

Purpose: The purpose of this study is to investigate factors affecting social problem-solving ability of alcohol-dependent patients with a focus on gender differences. Methods: Participants were 250 alcohol-dependent people(men 140, women 110) who were living in B, G and Y cities. Data were collected from January 10 to March 31, 2017 using self-report questionnaires. Abstinence self-efficacy, alcohol insight, unconditional self-acceptance, and social problem-solving ability were investigated. For data analysis, t-test, one-way ANOVA, Pearson correlation coefficients and multiple regression were employed. Results: Factors influencing social problem-solving ability for men were unconditional self-acceptance and age. The explanatory power was 28%. Factors influencing social problem-solving ability for women were unconditional self-acceptance, stress, religiousness, age, occupation and abstinence self-efficacy and the explanatory power was 72%. Unconditional self-acceptance and age were significant variables of social problem-solving ability in both men and women. Stress, occupation, religiousness and abstinence self-efficacy were significantly associated with social problem-solving ability in women but not in men. Conclusion: The results suggest that it is necessary to consider gender characteristics in order to develop effective management programs for social problem-solving ability in alcohol-dependent people.

• Research in Community and Public Health Nursing
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• v.28 no.4
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• pp.494-503
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• 2017

Purpose: This study is to grasp factors influencing the quality of life in older adults after total knee replacement. Methods: This study was conducted with 165 older adults who had TKR at four orthopedic hospitals in D city. Data were analyzed using one-way ANOVA, independent t-test, Pearson's correlation, and stepwise multiple linear regression with SPSS 19.0 software. Results: Pain and depression were negatively correlated with range of motion, social support, while sense of coherence was positively correlated with quality of life. Sense of coherence (43%, ${\beta}=.40$), pain (8%, ${\beta}=-.30$), and depression (3%, ${\beta}=-.20$) on the Physical Component Summary in the quality of life have significant explanatory power of 54%. Sense of coherence (49%, ${\beta}=.44$), social support (6%, ${\beta}=.25$), and depression (3%, ${\beta}=-.22$) on the Mental Component Summary in the quality of life have significant explanatory power of 58%. Conclusion: This study suggests developing a program to improve the quality of life in older adults who had TKR, considering factors such as sense of coherence.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

• Child Health Nursing Research
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• v.12 no.1
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• pp.15-24
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• 2006

Purpose: This study was done to define nursing behavior in neonatal intensive care units so as to estimate resource-based relative value-. Method: Participating in this study were 292 nurses in neonatal intensive care units. The study surveyed physical and mental labor, stress and time involved in nursing work. Tool used in this study was a nursing labor per relative value tool. For analyzes, the relative value of each nursing behavior was calculated, where the mean value of the three components, labor intensity and component-by-component explanatory power were in percentage terms. Results: 1. Nursing behaviors in neonatal intensive care unit were classified and defined at three levels: 5 main domains, 17 mid-domains, and 42 small domains. 2. The per component explanatory power of intensity involved in nursing labor showed physical effort to be 32.45%, mental 32.86%, and stress 34.69%. 3. The reliability of nursing labor factors was very strong, Cronbach's alpha value of 0.96. Conclusion: In this research, which is a first in defining nursing behavior in neonatal intensive care units, individual nursing behavior were broken down using resource-based relative value for nursing cost, and each nursing behavior was successfully translated to a numerical value.

• Korean Journal of Occupational Health Nursing
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• v.27 no.2
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• pp.121-130
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• 2018

Purpose: The purpose of this study was to investigate the effects of major health issues and job stress on presenteeism among clinical nurses. Methods: The investigator conducted a survey on 226 clinical nurses at a general hospital in Seoul from March 3 to April 15, 2017, and analyzed their responses. Results: The findings showed that job stress did not have a significant effect on the nurses' presenteeism. Fatigue (t=3.55,p<.001) impacted job loss, one of the subcategories of presenteeism, with an explanatory power of 12.1%. Premenstrual syndrome (t=-2.67,p=.008) and fatigue (t=-2.46,p=.015) affected perceived productivity with an explanatory power of 23.6%. Conclusion: Based on these findings, the study highlighted the need for effective management programs to tackle fatigue and premenstrual syndrome among clinical nurses' major health issues in order to reduce their productivity loss.